Hidden units are recruited during so-called input phases. Output weights are frozen (shown by solid large arrow). Eight candidate units each have initially random, trainable connection weights from the input units (shown by dashed large arrow). These input weights are adjusted in order to maximize a correlation between their activation and network error at the output units, over the training patterns.